EP2962300B1 - Method and apparatus for generating a speech signal - Google Patents

Method and apparatus for generating a speech signal Download PDF

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Publication number
EP2962300B1
EP2962300B1 EP14707461.1A EP14707461A EP2962300B1 EP 2962300 B1 EP2962300 B1 EP 2962300B1 EP 14707461 A EP14707461 A EP 14707461A EP 2962300 B1 EP2962300 B1 EP 2962300B1
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EP
European Patent Office
Prior art keywords
speech
microphone
signal
similarity
reverberant
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EP14707461.1A
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German (de)
English (en)
French (fr)
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EP2962300A1 (en
Inventor
Sriram Srinivasan
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Koninklijke Philips NV
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Koninklijke Philips NV
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/02—Casings; Cabinets ; Supports therefor; Mountings therein
    • H04R1/025—Arrangements for fixing loudspeaker transducers, e.g. in a box, furniture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/20—Arrangements for obtaining desired frequency or directional characteristics
    • H04R1/32—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only
    • H04R1/40—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/20—Arrangements for obtaining desired frequency or directional characteristics
    • H04R1/32—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only
    • H04R1/40—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers
    • H04R1/406—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers microphones
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2201/00—Details of transducers, loudspeakers or microphones covered by H04R1/00 but not provided for in any of its subgroups
    • H04R2201/02—Details casings, cabinets or mounting therein for transducers covered by H04R1/02 but not provided for in any of its subgroups
    • H04R2201/023—Transducers incorporated in garment, rucksacks or the like
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2420/00—Details of connection covered by H04R, not provided for in its groups
    • H04R2420/07—Applications of wireless loudspeakers or wireless microphones

Definitions

  • the invention relates to a method and apparatus for generating a speech signal, and in particular to generating a speech signal from a plurality of microphone signals, such as e.g. microphones in different devices.
  • devices owned and used by a user has increased substantially.
  • devices equipped with audio capture and typically wireless transmission are becoming increasingly common, such as e.g., mobile phones, tablet computers, notebooks, etc.
  • Another approach is to use hands free communication based on a microphone being positioned further away from the user.
  • conference systems have been developed which when positioned e.g. on a table will pick-up speakers located around the room.
  • such systems tend to not always provide optimum speech quality, and in particular the speech from more distant users tends to be weak and noisy.
  • the captured speech will in such scenarios tend to have a high degree of reverberation which may reduce the intelligibility of the speech substantially.
  • Document US 381-4856 teaches an apparatus, which is analog in nature and uses the output of one of the microphones as a reference.More specifically, the document describes a system where the measuring reference microphone measures background noise and for each program microphone it is determined whether the signal is above the background noise level. If this is the case there is apparently desired activity and the microphone selected. Thus the document does not disclose a comparison between the microphone signal and non-reverberant speech. The document only discloses a comparison between the microphone signal and a background noise level.
  • the document US 2011/038486 which is digital in nature, uses the output of the combined microphones as a reference.
  • a beam-former output is compared with a single microphone signal (selected as one of the signals from the beam) and a distortion calculator (possibly based on a reverberation estimate) determines whether the single microphone is selected or the output of the beam-former .
  • the document therefore does not disclose a comparison between the microphone signal and non-reverberant speech. In the document only a comparison is taught between the microphone and the beam-former output.
  • an improved approach for capturing speech signals would be advantageous and in particular an approach allowing increased flexibility, improved speech quality, reduced reverberation, reduced complexity, reduced communication requirements, increased adaptability for different devices (including multifunction devices), reduced resource demand and/or improved performance would be advantageous.
  • the Invention seeks to preferably mitigate, alleviate or eliminate one or more of the above mentioned disadvantages singly or in any combination.
  • the invention may allow an improved speech signal to be generated in many embodiments.
  • it may in many embodiments allow a speech signal to be generated with less reverberation and/or often less noise.
  • the approach may allow improved performance of speech applications, and may in particular in many scenarios and embodiments provide improved speech communication.
  • the comparison of at least one property derived from the microphone signals to a reference property for non-reverberant speech provides a particular efficient and accurate way of identifying the relative importance of the individual microphone signals to the speech signal, and may in particular provide a better evaluation than approaches based on e.g. signal level or signal-to-noise ratio measures. Indeed, the correspondence of the captured audio to non-reverberant speech signals may provide a strong indication of how much of the speech reaches the microphone via a direct path and how much reaches the microphone via reverberant paths.
  • the at least one reference property may be one or more properties/ values which are associated with non-reverberant speech.
  • the at least one reference property may be a set of properties corresponding to different samples of non-reverberant speech.
  • the similarity indication may be determined to reflect a difference between the value of the at least one property derived from the microphone signal and the at least one reference property for non-reverberant speech, and specifically to at least one reference property of one non-reverberant speech sample.
  • the at least one property derived from the microphone signal may be the microphone signal itself.
  • the at least one reference property for non-reverberant speech may be a non-reverberant speech signal.
  • the property may be an appropriate feature such as gain normalized spectral envelopes.
  • the microphones providing the microphone signals may in many embodiments be microphones distributed in an area, and may be remote from each other.
  • the approach may in particular provide improved usage of audio captured at different positions without requiring these positions to be known or assumed by the user or the apparatus/system.
  • the microphones may be randomly distributed in an ad-hoc fashion around a room, and the system may automatically adapt to provide an improved speech signal for the specific arrangement.
  • the non-reverberant speech samples may specifically be substantially dry or anechoic speech samples.
  • the speech similarity indication may be any indication of a degree of difference or similarity between the individual microphone signal (or part thereof) and non-reverberant speech, such as e.g. a non-reverberant speech sample.
  • the similarity indication may be a perceptual similarity indication.
  • the apparatus comprises a plurality of separate devices, each device comprising a microphone receiver for receiving at least one microphone signal of the plurality of microphone signals.
  • each device may comprise the microphone providing the microphone signal.
  • the invention may allow improved and/or new user experiences with improved performance.
  • a number of possible diverse devices may be positioned around a room.
  • the individual devices may each provide a microphone signal, and these may be evaluated to find the most suited devices/ microphones to use for generating the speech signal.
  • At least a first device of the plurality of separate devices comprises a local comparator for determining a first speech similarity indication for the at least one microphone signal of the first device.
  • This may provide an improved operation in many scenarios, and may in particular allow a distributed processing which may reduce e.g. communication resources and/or spread computational resource demands.
  • the separate devices may determine a similarity indication locally and may only transmit the microphone signal if the similarity criterion meets a criterion.
  • the generator is implemented in a generator device separate from at least the first device; and wherein the first device comprises a transmitter for transmitting the first speech similarity indication to the generator device.
  • the transmitter may be arranged to transmit the first speech similarity indication via a wireless communication link, such as a Bluetooth TM or Wi-Fi communication link.
  • the generator device is arranged to receive speech similarity indications from each of the plurality of separate devices, and wherein the generator is arranged to generate the speech signal using a subset of microphone signals from the plurality of separate devices, the subset being determined in response to the speech similarity indications received from the plurality of separate devices.
  • the subset may include only a single microphone.
  • the generator may be arranged to generate the speech signal from a single microphone signal selected from the plurality of microphone signals based on the similarity indications.
  • At least one device of the plurality of separate devices is arranged to transmit the at least one microphone signal of the at least one device to the generator device only if the at least one microphone signal of the at least one device is comprised in the subset of microphone signals.
  • the transmitter may be arranged to transmit the at least one microphone signal via a wireless communication link, such as a Bluetooth TM or Wi-Fi communication link.
  • the generator device comprises a selector arranged to determine the subset of microphone signals, and a transmitter for transmitting an indication of the subset to at least one of the plurality of separate devices.
  • the generator may determine the subset and may be arranged to transmit an indication of the subset to at least one device of the plurality of devices. For example, for the device or devices of microphone signals comprised in the subset, the generator may transmit an indication that the device should transmit the microphone signal to the generator.
  • the transmitter may be arranged to transmit the indication via a wireless communication link, such as a Bluetooth TM or Wi-Fi communication link.
  • a wireless communication link such as a Bluetooth TM or Wi-Fi communication link.
  • the comparator is arranged to determine the similarity indication for a first microphone signal in response to a comparison of at least one property derived from the microphone signal to reference properties for speech samples of a set of non-reverberant speech samples.
  • the comparison of microphone signals to a large set of non-reverberating speech samples provides a particular efficient and accurate way of identifying the relative importance of the individual microphone signals to the speech signal, and may in particular provide a better evaluation than approaches based on e.g. signal level or signal-to-noise ratio measures.
  • the correspondence of the captured audio to non-reverberant speech signals may provide a strong indication of how much of the speech reaches the microphone via a direct path and how much reaches the microphone via reverberant/ reflected paths.
  • the comparison to the non-reverberant speech samples includes a consideration of the shape of impulse response of the acoustic paths rather than just an energy or level consideration.
  • the approach may be speaker independent and in some embodiments the set of non-reverberant speech samples may include samples corresponding to different speaker characteristics (such as a high or low voice).
  • the processing may be segmented, and the set of non-reverberant speech samples may for example comprise samples corresponding to the phonemes of human speech
  • the comparator may for each microphone signal determine an individual similarity indication for each speech sample of the set of non-reverberant speech samples.
  • the similarity indication for the microphone signal may then be determined from the individual similarity indications, e.g. by selecting the individual similarity indication which is indicative of the highest degree of similarity. In many scenarios, the best matching speech sample may be identified and the similarity indication for the microphone signal may be determined with respect to this speech sample.
  • the similarity indication may provide an indication of a similarity of the microphone signal (or part thereof) to the non-reverberant speech sample of the set of non-reverberant speech samples for which the highest similarity is found.
  • the similarity indication for a given speech signal sample may reflect the likelihood that the microphone signal resulted from a speech utterance corresponding to the speech sample.
  • the speech samples of the set of non-reverberating speech samples are represented by parameters for a non-reverberating speech model.
  • the approach may in many embodiments reduce the computational and/or memory resource requirements.
  • the comparator may in some embodiments evaluate the model for the different sets of parameters and compare the resulting signals to the microphone signal(s). For example, frequency representations of the microphone signals and the speech samples may be compared.
  • model parameters for the speech model may be generated from the microphone signal, i.e. the model parameters which would result in a speech sample matching the microphone signal may be determined. These model parameters may then be compared to the parameters of the set of non-reverberant speech samples.
  • the non-reverberating speech model may specifically be a Linear Prediction model, such as a CELP (Code-Excited Linear Prediction) model.
  • a Linear Prediction model such as a CELP (Code-Excited Linear Prediction) model.
  • the comparator is arranged to determine a first reference property for a first speech sample of the set of non-reverberating speech samples from a speech sample signal generated by evaluating the non-reverberating speech model using the parameters for the first speech sample, and to determine the similarity indication for a first microphone signal of the plurality of microphone signals in response to a comparison of the property derived from the first microphone signal and the first reference property.
  • the similarity indication for the first microphone signal may be determined by comparing a property determined for the first microphone signal to reference properties determined for each of the non-reverberant speech samples, the reference properties being determined from a signal representation generated by evaluating the model.
  • the comparator may compare a property of the microphone signal to a property of the signal samples resulting from evaluating the non-reverberating speech model using the stored parameters for the non-reverberant speech samples.
  • the comparator is arranged to decompose a first microphone signal of the plurality of microphone signals into a set of basis signal vectors; and to determine the similarity indication in response to a property of the set of basis signal vectors.
  • the reference property may be related to a set of basis vectors in an appropriate feature domain, from which a non-reverberant feature vector can be generated as a weighted sum of basis vectors.
  • This set can be designed such that a weighted sum with only a few basis vectors is sufficient to accurately describe the non-reverberant feature vector, i.e., the set of basis vectors provides a sparse representation for non-reverberant speech.
  • the reference property may be the number of basis vectors that appear in the weighted sum.
  • the property may be the number of basis vectors that receive a non-zero weight (or a weight above a given threshold) when used to describe a feature vector extracted from the microphone signal.
  • the similarity indication may indicate an increasing similarity to non-reverberant speech for a reducing number of basic signal vectors.
  • the comparator is arranged to determine speech similarity indications for each segment of a plurality of segments of the speech signal, and the generator is arranged to determine combination parameters for the combining for each segment.
  • the apparatus may utilize segmented processing.
  • the combination may be constant for each segment but may be varied from one segment to the next.
  • the speech signal may be generated by selecting one microphone signal in each segment.
  • the combination parameters may for example be combination weights for the microphone signal or may e.g. be a selection of a subset of microphone signals to include in the combination.
  • the approach may provide improved performance and/or facilitated operation.
  • the generator is arranged to determine combination parameters for one segment in response to similarity indications of at least one previous segment.
  • This may provide improved performance in many scenarios. For example, it may provide a better adaptation to slow changes, and may reduce disruptions in the generated speech signal.
  • the combination parameters may be determined only based on segments containing speech and not on segments during quiet periods or pauses.
  • the generator is arranged to determine combination parameters for a first segment in response to a user motion model.
  • the generator is arranged to select a subset of the microphone signals to combine in response to the similarity indications.
  • the combining may specifically be selection combining.
  • the generator may specifically select only microphone signals for which the similarity indication meets an absolute or relative criterion.
  • the subset of microphone signals comprise only one microphone signal.
  • the generator is arranged to generate the speech signal as a weighted combination of the microphone signals, a weight for a first of the microphone signals depending on the similarity indication for the microphone signal.
  • This may allow improved and/or facilitated operation in many embodiments.
  • a method of generating a speech signal comprising: receiving microphone signals from a plurality of microphones; for each microphone signal, determining a speech similarity indication indicative of a similarity between the microphone signal and non-reverberant speech, the similarity indication being determined in response to a comparison of at least one property derived from the microphone signal to at least one reference property for non-reverberant speech; and generating the speech signal by combining the microphone signals in response to the similarity indications.
  • the comparator may be arranged to determine the similarity indication in response to a comparison performed in the feature domain.
  • the comparator may be arranged to determine some features/parameters from the microphone signal and compare these to stored features/ parameters for non-reverberant speech. For example, as will be described in more detail later, the comparison may be based on parameters for a speech model, such as coefficients for a linear prediction model. Corresponding parameters may then be determined for the microphone signal and compared to stored parameters corresponding to various utterances in an anechoic environment.
  • the apparatus of FIG. 1 utilizes an approach that allows the speech reverberation characteristic for the individual microphones to be assessed such that this can be taken into consideration. Indeed, the Inventor has realized not only that considerations of speech reverberation characteristics for individual microphone signals when generating a speech signal may improve quality substantially, but also how this can feasibly be achieved without requiring dedicated test signals and measurements. Indeed, the Inventor has realized that by comparing a property of the individual microphone signals with a reference property associated with non-reverberant speech, and specifically with sets of non-reverberant speech samples, it is possible to determine suitable parameters for combining the microphone signals to generate an improved speech signal.
  • the speech signal may be communicated to a remote user, e.g. via a telephone network, a wireless connection, the Internet or any other communication network or link.
  • the communication of the speech signal may typically include a speech encoding as well as potentially other processing.
  • a similarity indication may be generated for each microphone signal in a given segment. For example, a microphone signal segment of, say, 50 msec duration may be generated for each of the microphone signals. The segment may then be compared to the set of non-reverberant speech samples which itself may be comprised of speech segment samples. The similarity indications may be determined for this 50 msec segment, and the generator 107 may proceed to generate a speech signal segment for the 50 msec interval based on the microphone signal segments and the similarity indications for the segment/ interval. Thus, the combination may be updated for each segment, e.g. by in each segment selecting the microphone signal which has the highest similarity to a speech segment sample of the non-reverberant speech samples.
  • the combination parameters such as a selection of a subset of microphone signals to use, or weights for a linear summation, may be determined for a time interval of the speech signal.
  • the speech signal may be determined in segments from a combination which is based on parameters that are constant for the segment but which may vary between segments.
  • the determination of combination parameters is independent for each time segment, i.e. the combination parameters for the time segment may be calculated based only on similarity indications that are determined for that time segment.
  • the combination parameters may alternatively or additionally be determined in response to similarity indications of at least one previous segment.
  • the similarity indications may be filtered using a low pass filter that extends over several segments. This may ensure a slower adaptation which may e.g. reduce fluctuations and variations in the generated speech signal.
  • a hysteresis effect may be applied which prevents e.g. quick ping-pong switching between two microphones positioned at roughly the same distance from a speaker.
  • the generator 107 may be arranged to determine combination parameters for a first segment in response to a user motion model. Such an approach may be used to track the relative position of the user relative to the microphone devices 201, 203, 205.
  • the user model need not explicitly track positions of the user or the microphone devices 201, 203, 205 but may directly track the variations of the similarity indications.
  • a state-space representation may be employed to describe a human motion model and a Kalman filter may be applied to the similarity indications of the individual segments of one microphone signal in order to track the variations of the similarity indications due to movement. The resulting output of the Kalman filter may then be used as the similarity indication for the current segment.
  • each of the microphones 103 may be part of or connected to a different device, and thus the microphone receivers 101 may be comprised in different devices.
  • the similarity processor 105 and generator 107 are implemented in a single device.
  • a number of different remote devices may transmit a microphone signal to a generator device which is arranged to generate a speech signal from the received microphone signals.
  • This generator device may implement the functionality of the similarity processor 105 and the generator 107 as previously described.
  • each of the devices may comprise a (sub)similarity processor 105 which is arranged to determine a similarity indication for the microphone signal of that device.
  • the similarity indications may then be transmitted to the generator device which may determine parameters for the combination based on the received similarity indications. For example, it may simply select the microphone signal/ device which has the highest similarity indication.
  • the devices may not transmit microphone signals to the generator device unless the generator device requests this. Accordingly, the generator device may transmit a request for the microphone signal to the selected device which in return provides this signal to the generator device. The generator device then proceeds to generate the output signal based on the received microphone signal.
  • the generator 107 may be considered to be distributed over the devices with the combination being achieved by the process of selecting and selectively transmitting the microphone signal.
  • the approach may use microphones of devices distributed in an area of interest in order to capture a user's speech.
  • a typical modern living room typically has a number of devices equipped with one or more microphones and wireless transmission capabilities. Examples include cordless fixed-line phones, mobile phones, video chat-enabled televisions, tablet PCs, laptops, etc.
  • These devices may in some embodiments be used to generate a speech signal, e.g. by automatically and adaptively selecting the speech captured by the microphone closest to the speaker. This may provide captured speech which typically will be of high quality and free from reverberation.
  • the signal captured by a microphone will tend to be affected by reverberation, ambient noise and microphone noise with the impact depending on its location with respect to the sound source, e.g., to the user's mouth.
  • the system may seek to select the microphone which is closest to that which would be recorded by a microphone close to the user's mouth.
  • the generated speech signal can be applied where hands-free speech capture is desirable such as e.g., home/office telephony, tele-conferencing systems, front-end for voice control systems, etc.
  • FIG. 2 illustrates an example of a distributed speech generating/capturing apparatus/system.
  • the example includes a plurality of microphone devices 201, 203, 205 as well as a generator device 207.
  • the similarity processor 105 of each microphone device 201, 203, 205 specifically performs the operation of the similarity processor 105 of FIG. 1 for the specific microphone signal of the individual microphone device 201, 203, 205.
  • the similarity processor 105 of each of the microphone devices 201, 203, 205 specifically proceeds to compare the microphone signal to a set of non-reverberant speech samples which are locally stored in each of the devices.
  • the similarity processor 105 may specifically compare the microphone signal to each of the non-reverberant speech samples and for each speech sample determine an indication of how similar the signals are.
  • the microphone devices 201, 203, 205 and the generator device 207 may be arranged to communicate data both directions. However, it will be appreciated that in some embodiments, only one-way communication from the microphone devices 201, 203, 205 to the generator device 207 may be applied.
  • the devices may communicate via a wireless communication network such as a local Wi-Fi communication network.
  • a wireless communication network such as a local Wi-Fi communication network.
  • the wireless transceiver 207 of the microphone devices 201, 203, 205 may specifically be arranged to communicate with other devices (and specifically with the generator device 207) via Wi-Fi communications.
  • other communication methods may be used including for example communication over e.g. a wired or wireless Local Area Network, Wide Area Network, the Internet, Bluetooth TM communication links etc.
  • the wireless transceiver 211 of the generator device 207 is coupled to a controller 213 and a speech signal generator 215.
  • the controller 213 is fed the similarity indications from the wireless transceiver 211 and in response to these it determines a set of combination parameters which control how the speech signal is generated from the microphone signals.
  • the controller 213 is coupled to the speech signal generator 215 which is fed the combination parameters.
  • the speech signal generator 215 is fed the microphone signals from the wireless transceiver 211, and it may accordingly proceed to generate the speech signal based on the combination parameters.
  • the controller 213 may compare the received similarity indications and identify the one indicating the highest degree of similarity. An indication of the corresponding device/ microphone signal may then be passed to the speech signal generator 215 which can proceed to select the microphone signal from this device. The speech signal is then generated from this microphone signal.
  • the microphone devices 201, 203, 205 transmits a microphone signal.
  • Such an approach may substantially reduce the communication resource usage as well as reduce e.g. power consumption of the individual devices. It may also substantially reduce the complexity of the generator device 207 as this only needs to deal with e.g. one microphone signal at a time.
  • the selection combining functionality used to generate the speech signal is thus distributed over the devices.
  • the non-reverberating speech model may be a linear prediction model, such as specifically a CELP (Code Excited Linear Prediction) model.
  • each speech sample of the non-reverberant speech samples may be represented by a codebook entry which specifies an excitation signal that may be used to excite a synthesis filter (which may also be represented by the stored parameters).
  • K microphones may be distributed in an area.
  • the impulse response h k ( n ) corresponds to a pure delay, corresponding to the time taken for the signal to propagate from the point of generation to the microphone at the speed of sound. Consequently, the PSD of the signal x k ( n ) is identical to that of s ( n ).
  • h k ( n ) models not only the direct path of the signal from the sound source to the microphone but also signals arriving at the microphone as a result of being reflected by walls, ceiling, furniture, etc. Each reflection delays and attenuates the signal.
  • the covariance matrices can be described as circulant and are diagonalized by the Fourier transform.
  • the logarithm of the likelihood in the above equation, corresponding to the i th speech codebook vector a i can then be written using frequency domain quantities as (refer e.g. U. Grenander and G. Szego, "Toeplitz forms and their applications", 2nd ed.
  • the noisy PSD P y k ( ⁇ ) and the noise PSD P w k ( ⁇ ) can be estimated from the microphone signal, and Ai ( ⁇ ) is specified by the i th codebook vector.
  • Ai ( ⁇ ) is specified by the i th codebook vector.
  • L k * max 1 ⁇ i ⁇ I L k i , 1 ⁇ k ⁇ K , where I is the number of vectors in the speech codebook. This maximum likelihood value is then used as the similarity indication for the specific microphone signal.
  • the codebook size was fixed at 256 entries.
  • the impulse response between the location of the speaker and each of the three microphones was recorded and then convolved with a dry speech signal to obtain the microphone data.
  • the microphone noise at each microphone was 40 dB below the speech level.
  • a particular advantage of the approach is that it inherently compensates for signal level differences between the different microphones.
  • the approach selects the appropriate microphone during speech activity.
  • non-speech segments such as e.g. pauses in the speech or when the speaker changes
  • a speech activity detector such as a simple level detector
  • the system may simply proceed using the combination parameters determined for the last segment which included a speech component.
  • a set of properties may be derived by analyzing the microphone signals and these properties may then be compared to expected values for non-reverberant speech.
  • the comparison may be performed in the parameter or property domain without consideration of specific non-reverberant speech samples.
  • the similarity processor 105 may be arranged to decompose the microphone signals using a set of basis signal vectors.
  • a decomposition may specifically use a sparse overcomplete dictionary that contains signal prototypes, also called atoms.
  • a signal is then described as a linear combination of a subset of the dictionary.
  • each atom may in this case correspond to a basis signal vector.
  • the property derived from the microphone signals and used in the comparison may be the number of basis signal vectors, and specifically the number of dictionary atoms, that are needed to represent the signal in an appropriate feature domain.
  • the property may then be compared to one or more expected properties for non-reverberant speech.
  • the values for the set of basis vectors may be compared to samples of values for sets of basis vector corresponding to specific non-reverberant speech samples.
  • the one that can be described using fewer dictionary atoms is more similar to non-reverberant speech (where the dictionary has been trained on non-reverberant speech).
  • the number of basis vectors for which the value (specifically the weight of each basis vector in a combination of basis vectors approximating the signal) exceeds a given threshold may be used to determine the similarity indication.
  • the number of basis vectors which exceed the threshold may simply be calculated and directly used as the similarity indication for a given microphone signal, with an increasing number of basis vectors indicating a reduced similarity.
  • the property derived from the microphone signal may be the number of basis vector values that exceed a threshold, and this may be compared to a reference property for non-reverberant speech of zero or one basis vectors having values above the threshold.
  • the invention can be implemented in any suitable form including hardware, software, firmware or any combination of these.
  • the invention may optionally be implemented at least partly as computer software running on one or more data processors and/or digital signal processors.
  • the elements and components of an embodiment of the invention may be physically, functionally and logically implemented in any suitable way. Indeed the functionality may be implemented in a single unit, in a plurality of units or as part of other functional units. As such, the invention may be implemented in a single unit or may be physically and functionally distributed between different units, circuits and processors.

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  • Physics & Mathematics (AREA)
  • Otolaryngology (AREA)
  • Human Computer Interaction (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Multimedia (AREA)
  • Computational Linguistics (AREA)
  • Quality & Reliability (AREA)
  • General Health & Medical Sciences (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
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RU2648604C2 (ru) 2018-03-26
US20150380010A1 (en) 2015-12-31
CN105308681A (zh) 2016-02-03
BR112015020150B1 (pt) 2021-08-17
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